在R中提取共享时间持续时间的共同词

发布于 2025-01-17 15:09:23 字数 1720 浏览 2 评论 0原文

我需要从已经包含单变量异常的数据框中提取联合异常。

# Libraries
library(dplyr)
library(lubridate)
library(stringr)

# Create input dataframe
DF <- data.frame(
  rowID = as.factor(c(1,2,3,4,5,6,7,8)),
  Start = as_datetime(c('2022-01-01 09:00:00', '2022-01-01 12:00:00', '2022-01-02 15:00:00',
                        '2022-01-02 23:30:00', '2022-01-03 00:10:00', '2022-01-29 00:10:00',
                        '2023-12-25 06:00:00', '2023-12-25 08:00:00')),
  Finish = as_datetime(c('2022-01-01 11:00:00', '2022-01-01 15:00:00','2022-01-03 01:00:00',
                         '2022-01-02 23:50:00', '2022-01-03 03:00:00', '2022-01-31 03:00:00',
                         '2023-12-25 11:00:00', '2023-12-25 12:00:00')),
  Process = c('Process1', 'Process2', 'Process1', 'Process2', 'Process3', 'Process3', 'Process3', 'Process3'),
  Anomaly = c('Y','N','Y','Y','Y', 'Y', 'Y', 'Y')
) %>%
  arrange(Start, Process) %>%
  mutate(Interval = interval(Start, Finish)) %>%
  as_tibble()

我能够成功标记与感兴趣的进程 (Process3) 类似的时间段内发生的协同异常。

# Declare process of interest
c <- 'Process3'

# Extract co-anomalies within and between Process3
Result <- DF %>%
  filter(int_overlaps(Interval, Interval[Process == c]) == TRUE) %>%
  mutate(coAnomaly = ifelse(Anomaly == 'Y', 'Y', 'N')) %>%
  left_join(DF, ., by = c('rowID' = 'rowID')) %>%
  select(contains('.x'), coAnomaly) %>%
  rename_with(~str_remove(., '.x'))

该代码正确标记了进程 3 和其他进程之间的协同异常。尽管它在检测进程 3 自身时会出错。

第 6 行是一个错误，该异常不会在另一个 Process3 内或任何其他进程之间同时发生。

我正在尝试正确标记：

哪个 Process3s 与其他进程共同发生（在 LHS 之间）
哪个其他进程与 Process3s 共同发生（在 RHS 之间）
哪个 Process3s 与 Process3s 共同发生（内部）

原文

I need to extract co-anomalies from a data-frame which already contains univariate anomalies.

# Libraries
library(dplyr)
library(lubridate)
library(stringr)

# Create input dataframe
DF <- data.frame(
  rowID = as.factor(c(1,2,3,4,5,6,7,8)),
  Start = as_datetime(c('2022-01-01 09:00:00', '2022-01-01 12:00:00', '2022-01-02 15:00:00',
                        '2022-01-02 23:30:00', '2022-01-03 00:10:00', '2022-01-29 00:10:00',
                        '2023-12-25 06:00:00', '2023-12-25 08:00:00')),
  Finish = as_datetime(c('2022-01-01 11:00:00', '2022-01-01 15:00:00','2022-01-03 01:00:00',
                         '2022-01-02 23:50:00', '2022-01-03 03:00:00', '2022-01-31 03:00:00',
                         '2023-12-25 11:00:00', '2023-12-25 12:00:00')),
  Process = c('Process1', 'Process2', 'Process1', 'Process2', 'Process3', 'Process3', 'Process3', 'Process3'),
  Anomaly = c('Y','N','Y','Y','Y', 'Y', 'Y', 'Y')
) %>%
  arrange(Start, Process) %>%
  mutate(Interval = interval(Start, Finish)) %>%
  as_tibble()

I'm able to successfully tag co-anomalies which occurred over similar time periods as the process of interest (Process3).

# Declare process of interest
c <- 'Process3'

# Extract co-anomalies within and between Process3
Result <- DF %>%
  filter(int_overlaps(Interval, Interval[Process == c]) == TRUE) %>%
  mutate(coAnomaly = ifelse(Anomaly == 'Y', 'Y', 'N')) %>%
  left_join(DF, ., by = c('rowID' = 'rowID')) %>%
  select(contains('.x'), coAnomaly) %>%
  rename_with(~str_remove(., '.x'))

The code correctly tags co-anomalies between process 3 and other processes. Although it makes errors when detecting process 3 against itself.

Row 6 is an error, the anomaly doesn't co-occur within another Process3 or between any other process.

I'm trying to correctly tag:

Which Process3s co-occurred with other-processes (Between LHS)
Which other-processes co-occurred with Process3s (Between RHS)
Which Process3s co-occurred with Process3s (Within)

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面犯桃花 2025-01-24 15:09:23

continue

You can try this approach using rowwise():

left_join(DF, DF %>%
  rowwise() %>% 
  filter(any(int_overlaps(Interval, DF$Interval[which(DF$rowID!=rowID & DF$Process == c)]))) %>% 
  mutate(coAnomaly = ifelse(Anomaly == 'Y', 'Y', 'N')) %>%
  select(rowID, coAnomaly)
)

Output:

  rowID Start               Finish              Process  Anomaly Interval                                         coAnomaly
  <fct> <dttm>              <dttm>              <chr>    <chr>   <Interval>                                       <chr>    
1 1     2022-01-01 09:00:00 2022-01-01 11:00:00 Process1 Y       2022-01-01 09:00:00 UTC--2022-01-01 11:00:00 UTC NA       
2 2     2022-01-01 12:00:00 2022-01-01 15:00:00 Process2 N       2022-01-01 12:00:00 UTC--2022-01-01 15:00:00 UTC NA       
3 3     2022-01-02 15:00:00 2022-01-03 01:00:00 Process1 Y       2022-01-02 15:00:00 UTC--2022-01-03 01:00:00 UTC Y        
4 4     2022-01-02 23:30:00 2022-01-02 23:50:00 Process2 Y       2022-01-02 23:30:00 UTC--2022-01-02 23:50:00 UTC NA       
5 5     2022-01-03 00:10:00 2022-01-03 03:00:00 Process3 Y       2022-01-03 00:10:00 UTC--2022-01-03 03:00:00 UTC NA       
6 6     2022-01-29 00:10:00 2022-01-31 03:00:00 Process3 Y       2022-01-29 00:10:00 UTC--2022-01-31 03:00:00 UTC NA

Updated, given OP's additional request of separating Between/Within, and new frame:

rbind(
  DF %>% 
    filter(Process==c) %>% 
    rowwise() %>% 
    filter(any(int_overlaps(Interval, DF$Interval[which(DF$rowID!=rowID & DF$Process == c)]))) %>% 
    mutate(coAnomaly = "within"),
  
  DF %>% 
    filter(Process!=c) %>% 
    rowwise() %>% 
    filter(any(int_overlaps(Interval, DF$Interval[which(DF$rowID!=rowID & DF$Process == c)]))) %>% 
    mutate(coAnomaly = "between")
)

Output:

  rowID Start               Finish              Process  Anomaly Interval                                         coAnomaly
  <fct> <dttm>              <dttm>              <chr>    <chr>   <Interval>                                       <chr>    
1 7     2023-12-25 06:00:00 2023-12-25 11:00:00 Process3 Y       2023-12-25 06:00:00 UTC--2023-12-25 11:00:00 UTC within   
2 8     2023-12-25 08:00:00 2023-12-25 12:00:00 Process3 Y       2023-12-25 08:00:00 UTC--2023-12-25 12:00:00 UTC within   
3 3     2022-01-02 15:00:00 2022-01-03 01:00:00 Process1 Y       2022-01-02 15:00:00 UTC--2022-01-03 01:00:00 UTC between

All types of overlaps:

Here is another approach, which does not depend on indicating a Process of interest (i.e. no need for c="Process3".

Create a small function that takes an interval, and the id and returns a tibble of overlapping ID (oID) and overlapping process (oProcess)

get_overlap_IDs = function(interval,id) {
  DF %>% filter(int_overlaps(interval, DF$Interval)) %>%
    filter(rowID!=id) %>% 
    select(oID=rowID, oProcess=Process)
}

Apply the function rowwise and unnest

DF %>% 
  rowwise() %>%
  mutate(keys = list(get_overlap_IDs(Interval, rowID))) %>% 
  unnest(keys)

Output:

  rowID Start               Finish              Process  Anomaly Interval                                         oID   oProcess
  <fct> <dttm>              <dttm>              <chr>    <chr>   <Interval>                                       <fct> <chr>   
1 3     2022-01-02 15:00:00 2022-01-03 01:00:00 Process1 Y       2022-01-02 15:00:00 UTC--2022-01-03 01:00:00 UTC 4     Process2
2 3     2022-01-02 15:00:00 2022-01-03 01:00:00 Process1 Y       2022-01-02 15:00:00 UTC--2022-01-03 01:00:00 UTC 5     Process3
3 4     2022-01-02 23:30:00 2022-01-02 23:50:00 Process2 Y       2022-01-02 23:30:00 UTC--2022-01-02 23:50:00 UTC 3     Process1
4 5     2022-01-03 00:10:00 2022-01-03 03:00:00 Process3 Y       2022-01-03 00:10:00 UTC--2022-01-03 03:00:00 UTC 3     Process1
5 7     2023-12-25 06:00:00 2023-12-25 11:00:00 Process3 Y       2023-12-25 06:00:00 UTC--2023-12-25 11:00:00 UTC 8     Process3
6 8     2023-12-25 08:00:00 2023-12-25 12:00:00 Process3 Y       2023-12-25 08:00:00 UTC--2023-12-25 12:00:00 UTC 7     Process3

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